

import argparse

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


UpperCamelCase__		 =     16
UpperCamelCase__		 =     32







def 						lowerCamelCase__      ( __A    :Union[str, Any]	,__A    :Optional[int] = 1_6		):


       """simple docstring"""




       __snake_case       	=							AutoTokenizer.from_pretrained("""bert-base-cased"""		)
       __snake_case       	=							load_dataset("""glue"""	,"""mrpc"""		)

       def tokenize_function(__A    :str		):
              # max_length=None => use the model max length (it's actually the default)
              __snake_case       	=							tokenizer(examples["""sentence1"""]	,examples["""sentence2"""]	,truncation=UpperCamelCase_	,max_length=UpperCamelCase_		)
              return outputs

       # Apply the method we just defined to all the examples in all the splits of the dataset
       # starting with the main process first:
       with accelerator.main_process_first():
              __snake_case       	=							datasets.map(
                  UpperCamelCase_	,batched=UpperCamelCase_	,remove_columns=["""idx""", """sentence1""", """sentence2"""]	,)

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       __snake_case       	=							tokenized_datasets.rename_column("""label"""	,"""labels"""		)

       def collate_fn(__A    :Tuple		):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              __snake_case       	=							1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     __snake_case       	=							1_6
              elif accelerator.mixed_precision != "no":
                     __snake_case       	=							8
              else:
                     __snake_case       	=							None

              return tokenizer.pad(
                  UpperCamelCase_	,padding="""longest"""	,max_length=UpperCamelCase_	,pad_to_multiple_of=UpperCamelCase_	,return_tensors="""pt"""	,)

       # Instantiate dataloaders.
       __snake_case       	=							DataLoader(
           tokenized_datasets["""train"""]	,shuffle=UpperCamelCase_	,collate_fn=UpperCamelCase_	,batch_size=UpperCamelCase_	,drop_last=UpperCamelCase_		)
       __snake_case       	=							DataLoader(
           tokenized_datasets["""validation"""]	,shuffle=UpperCamelCase_	,collate_fn=UpperCamelCase_	,batch_size=UpperCamelCase_	,drop_last=(accelerator.mixed_precision == """fp8""")	,)

       return train_dataloader, eval_dataloader







def 						lowerCamelCase__      ( __A    :Tuple	,__A    :Optional[int]		):


       """simple docstring"""




       __snake_case       	=							Accelerator(cpu=args.cpu	,mixed_precision=args.mixed_precision		)
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       __snake_case       	=							config["""lr"""]
       __snake_case       	=							int(config["""num_epochs"""]		)
       __snake_case       	=							int(config["""seed"""]		)
       __snake_case       	=							int(config["""batch_size"""]		)

       __snake_case       	=							evaluate.load("""glue"""	,"""mrpc"""		)

       # If the batch size is too big we use gradient accumulation
       __snake_case       	=							1
       if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
              __snake_case       	=							batch_size // MAX_GPU_BATCH_SIZE
              __snake_case       	=							MAX_GPU_BATCH_SIZE

       set_seed(UpperCamelCase_		)
       __snake_case	,    __snake_case       	=							get_dataloaders(UpperCamelCase_	,UpperCamelCase_		)
       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       __snake_case       	=							AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""	,return_dict=UpperCamelCase_		)

       # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
       # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
       # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
       __snake_case       	=							model.to(accelerator.device		)
       # Instantiate optimizer
       __snake_case       	=							AdamW(params=model.parameters()	,lr=UpperCamelCase_		)

       # Instantiate scheduler
       __snake_case       	=							get_linear_schedule_with_warmup(
           optimizer=UpperCamelCase_	,num_warmup_steps=1_0_0	,num_training_steps=(len(UpperCamelCase_		) * num_epochs) // gradient_accumulation_steps	,)

       # Prepare everything
       # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
       # prepare method.

       __snake_case	,    __snake_case	,    __snake_case	,    __snake_case	,    __snake_case       	=							accelerator.prepare(
           UpperCamelCase_	,UpperCamelCase_	,UpperCamelCase_	,UpperCamelCase_	,UpperCamelCase_		)

       # Now we train the model
       for epoch in range(UpperCamelCase_		):
              model.train()
              for step, batch in enumerate(UpperCamelCase_		):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device		)
                     __snake_case       	=							model(**UpperCamelCase_		)
                     __snake_case       	=							outputs.loss
                     __snake_case       	=							loss / gradient_accumulation_steps
                     accelerator.backward(UpperCamelCase_		)
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

              model.eval()
              for step, batch in enumerate(UpperCamelCase_		):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device		)
                     with torch.no_grad():
                            __snake_case       	=							model(**UpperCamelCase_		)
                     __snake_case       	=							outputs.logits.argmax(dim=-1		)
                     __snake_case	,    __snake_case       	=							accelerator.gather_for_metrics((predictions, batch["""labels"""])		)
                     metric.add_batch(
                         predictions=UpperCamelCase_	,references=UpperCamelCase_	,)

              __snake_case       	=							metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F'epoch {epoch}:'	,UpperCamelCase_		)







def 						lowerCamelCase__      ( ):


       """simple docstring"""




       __snake_case       	=							argparse.ArgumentParser(description="""Simple example of training script."""		)
       parser.add_argument(
           """--mixed_precision"""	,type=UpperCamelCase_	,default=UpperCamelCase_	,choices=["""no""", """fp16""", """bf16""", """fp8"""]	,help="""Whether to use mixed precision. Choose"""
           """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
           """and an Nvidia Ampere GPU."""	,)
       parser.add_argument("""--cpu"""	,action="""store_true"""	,help="""If passed, will train on the CPU."""		)
       __snake_case       	=							parser.parse_args()
       __snake_case       	=							{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
       training_function(UpperCamelCase_	,UpperCamelCase_		)


if __name__ == "__main__":
    main()


# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

_SCREAMING_SNAKE_CASE 		=      """tiny-wmt19-en-ru"""

# Build

# borrowed from a test
_SCREAMING_SNAKE_CASE 		=      [
    """l""",
    """o""",
    """w""",
    """e""",
    """r""",
    """s""",
    """t""",
    """i""",
    """d""",
    """n""",
    """w</w>""",
    """r</w>""",
    """t</w>""",
    """lo""",
    """low""",
    """er</w>""",
    """low</w>""",
    """lowest</w>""",
    """newer</w>""",
    """wider</w>""",
    """<unk>""",
]
_SCREAMING_SNAKE_CASE 		=      dict(zip(vocab, range(len(vocab))))
_SCREAMING_SNAKE_CASE 		=      ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

with tempfile.TemporaryDirectory() as tmpdirname:
     _SCREAMING_SNAKE_CASE 		=      Path(tmpdirname)
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""src_vocab_file"""]
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""tgt_vocab_file"""]
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""merges_file"""]
     with open(src_vocab_file, """w""") as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(tgt_vocab_file, """w""") as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(merges_file, """w""") as fp:
          fp.write("""\n""".join(merges))

     _SCREAMING_SNAKE_CASE 		=      FSMTTokenizer(
         langs=["""en""", """ru"""],
         src_vocab_size=len(vocab),
         tgt_vocab_size=len(vocab),
         src_vocab_file=src_vocab_file,
         tgt_vocab_file=tgt_vocab_file,
         merges_file=merges_file,
     )

_SCREAMING_SNAKE_CASE 		=      FSMTConfig(
    langs=["""ru""", """en"""],
    src_vocab_size=1_0_0_0,
    tgt_vocab_size=1_0_0_0,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

_SCREAMING_SNAKE_CASE 		=      FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
_SCREAMING_SNAKE_CASE 		=      tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_SCREAMING_SNAKE_CASE 		=      tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-ru
